基于QR分解的双天线快速探测方法OA
Dual-Antenna Fast Detection Method Based on QR Decomposition
针对传统时间反转多重信号分类(Multiple Signal Classification,MUSIC)算法在探测中依赖大规模天线阵列、计算复杂度高且实时性差的问题,文章提出一种基于迭代QR分解的共偏移空频(QR Common-offset Space-Frequency,QR-CSF)成像算法.该方法通过共偏移距测量架构建立空频多态响应矩阵,仅需双天线配置即可获取时间反转算子,显著降低了系统复杂度;同时,利用迭代QR分解并求解噪声子空间,将算法核心复杂度从超线性降至亚线性阶,有效提升了计算效率.实验结果表明,QR-CSF算法在多目标场景中能够精准定位目标,空间响应强度分布契合目标物理属性,在提升成像分辨率的同时,将运算时长压缩至传统MUSIC方法的15%,成像效率获得显著提升.
To address the limitations of conventional time-reversal Multiple Signal Classification(MUSIC)algorithms,including dependency on large-scale antenna arrays,high computational complexity,and poor real-time performance,a QR common-offset space-frequency(QR-CSF)imaging algorithm based on iterative QR decomposition is proposed.The method establishes a space-frequency multistate response matrix through a co-offset measurement architecture,en-abling time-reversal operator acquisition with only a dual-antenna configuration,thereby significantly reducing system complexity.Simultaneously,iterative QR decomposition is employed to solve the noise subspace,reducing the core al-gorithmic complexity from superlinear to sublinear order and effectively enhancing computational efficiency.Experi-mental results demonstrate that the QR-CSF algorithm achieves precise target localization in multi-target scenarios,with spatial response intensity distributions closely matching target physical properties.While improving imaging resolution,it compresses computation time to 15%of conventional MUSIC methods,achieving a significant improvement in imag-ing efficiency
张乐乐;苏甜甜;周远国
西安科技大学通信与信息工程学院,陕西 西安 710054西安科技大学通信与信息工程学院,陕西 西安 710054西安科技大学通信与信息工程学院,陕西 西安 710054
信息技术与安全科学
时间反转多重信号分类算法迭代QR分解
time reversalmultiple signal classification algorithmiterative QR decomposition
《海军航空大学学报》 2026 (3)
565-573,582,10
国家自然科学基金(92166107)陕西省自然科学基金(2024JC-YBMS-556)
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